To train the module responsible for crowd counting by density estimation, we exploit many popular publicly available real-world datasets: ShanghaiTech [1]}, UCF-QNRF [2]} and NWPU-Crowd [3]}. Furthermore, also in this case, we exploit also a synthetic dataset, named gcc [4]}. These datasets are described in details in sec:back:datasets. To train this module, we adopt the supervised domain adaptation strategy illustrated in ch:virtual-to-real, consisting of training the network with the synthetic data and then fine-tuning it exploiting the real-world images. In particular, we set the initial weights of the network layers with values coming from a Gaussian distribution with 0.01 standard deviation. Then, we train the network exploiting the GCC dataset, and, finally, we fine-tune it using the real-world data.
